HR Analytics Employee Attrition And Performance

BCon 147: special topics

Author

Abegail G. Macabata

Published

October 24, 2024

1 Project overiew

In this project, we will explore employee attrition and performance using the HR Analytics Employee Attrition & Performance dataset. The primary goal is to develop insights into the factors that contribute to employee attrition. By analyzing a range of factors, including demographic data, job satisfaction, work-life balance, and job role, we aim to help businesses identify key areas where they can improve employee retention.

2 Scenario

Imagine you are working as a data analyst for a mid-sized company that is experiencing high employee turnover, especially among high-performing employees. The company has been facing increased costs related to hiring and training new employees, and management is concerned about the negative impact on productivity and morale. The human resources (HR) team has collected historical employee data and now looks to you for actionable insights. They want to understand why employees are leaving and how to retain talent effectively.

Your task is to analyze the dataset and provide insights that will help HR prioritize retention strategies. These strategies could include interventions like revising compensation policies, improving job satisfaction, or focusing on work-life balance initiatives. The success of your analysis could lead to significant cost savings for the company and an increase in employee engagement and performance.

3 Understanding data source

The dataset used for this project provides information about employee demographics, performance metrics, and various satisfaction ratings. The dataset is particularly useful for exploring how factors such as job satisfaction, work-life balance, and training opportunities influence employee performance and attrition.

This dataset is well-suited for conducting in-depth analysis of employee performance and retention, enabling us to build predictive models that identify the key drivers of employee attrition. Additionally, we can assess the impact of various organizational factors, such as training and work-life balance, on both performance and retention outcomes.

## datatable function from DT package create an HTML widget display of the dataset
## install DT package if the package is not yet available in your R environment
readxl::read_excel("dataset/dataset-variable-description.xlsx") |> 
  DT::datatable()

4 Data wrangling and management

Libraries

Task: Load the necessary libraries

Before we start working on the dataset, we need to load the necessary libraries that will be used for data wrangling, analysis and visualization. Make sure to load the following libraries here. For packages to be installed, you can use the install.packages function. There are packages to be installed later on this project, so make sure to install them as needed and load them here.

# load all your libraries here

options(repos = c(CRAN = "https://cran.rstudio.com"))

if (!require("ggplot2")) install.packages("ggplot2")
if (!require("sjPlot")) install.packages("sjPlot")
if (!require("ggstatsplot")) install.packages("ggstatsplot")
if (!require("dplyr")) install.packages("dplyr")
if (!require("tidyr")) install.packages("tidyr")
if (!require("GGally")) install.packages("GGally")
if (!require("report")) install.packages("report")


library(dplyr)
library(ggplot2)
library(DT)
library(readxl)
library(janitor)
library (GGally)
library(sjPlot)
library(report)
library(ggstatsplot)
library(tidyr)

4.1 Data importation

Task 4.1. Merging dataset

  • Import the two dataset Employee.csv and PerformanceRating.csv. Save the Employee.csv as employee_dta and PerformanceRating.csv as perf_rating_dta.

  • Merge the two dataset using the left_join function from dplyr. Use the EmployeeID variable as the varible to join by. You may read more information about the left_join function here.

  • Save the merged dataset as hr_perf_dta and display the dataset using the datatable function from DT package.

## import the two data here
library(readr)
Employee <- read_csv("dataset/Employee.csv")
View(Employee)

PerformanceRating <- read_csv("dataset/PerformanceRating.csv")
View(PerformanceRating)

employee_dta <- read_csv("Employee.csv")        
perf_rating_dta <- read_csv("PerformanceRating.csv")

## merge employee_dta and perf_rating_dta using left_join function.
## save the merged dataset as hr_perf_dta

hr_perf_dta <- left_join(employee_dta, perf_rating_dta, by = "EmployeeID")
print(hr_perf_dta)
# A tibble: 6,899 × 33
   EmployeeID FirstName LastName Gender   Age BusinessTravel Department
   <chr>      <chr>     <chr>    <chr>  <dbl> <chr>          <chr>     
 1 3012-1A41  Leonelle  Simco    Female    30 Some Travel    Sales     
 2 3012-1A41  Leonelle  Simco    Female    30 Some Travel    Sales     
 3 3012-1A41  Leonelle  Simco    Female    30 Some Travel    Sales     
 4 3012-1A41  Leonelle  Simco    Female    30 Some Travel    Sales     
 5 3012-1A41  Leonelle  Simco    Female    30 Some Travel    Sales     
 6 3012-1A41  Leonelle  Simco    Female    30 Some Travel    Sales     
 7 3012-1A41  Leonelle  Simco    Female    30 Some Travel    Sales     
 8 3012-1A41  Leonelle  Simco    Female    30 Some Travel    Sales     
 9 3012-1A41  Leonelle  Simco    Female    30 Some Travel    Sales     
10 CBCB-9C9D  Leonerd   Aland    Male      38 Some Travel    Sales     
# ℹ 6,889 more rows
# ℹ 26 more variables: `DistanceFromHome (KM)` <dbl>, State <chr>,
#   Ethnicity <chr>, Education <dbl>, EducationField <chr>, JobRole <chr>,
#   MaritalStatus <chr>, Salary <dbl>, StockOptionLevel <dbl>, OverTime <chr>,
#   HireDate <chr>, Attrition <chr>, YearsAtCompany <dbl>,
#   YearsInMostRecentRole <dbl>, YearsSinceLastPromotion <dbl>,
#   YearsWithCurrManager <dbl>, PerformanceID <chr>, ReviewDate <chr>, …
## Use the datatable from DT package to display the merged dataset
datatable(hr_perf_dta)

4.2 Data management

Task 4.2. Standardizing variable names

  • Using the clean_names function from janitor package, standardize the variable names by using the recommended naming of variables.

  • Save the renamed variables as hr_perf_dta to update the dataset.

## clean names using the janitor packages and save as hr_perf_dta
hr_perf_dta <- clean_names(hr_perf_dta)


## display the renamed hr_perf_dta using datatable function
head(hr_perf_dta)
# A tibble: 6 × 33
  employee_id first_name last_name gender   age business_travel department
  <chr>       <chr>      <chr>     <chr>  <dbl> <chr>           <chr>     
1 3012-1A41   Leonelle   Simco     Female    30 Some Travel     Sales     
2 3012-1A41   Leonelle   Simco     Female    30 Some Travel     Sales     
3 3012-1A41   Leonelle   Simco     Female    30 Some Travel     Sales     
4 3012-1A41   Leonelle   Simco     Female    30 Some Travel     Sales     
5 3012-1A41   Leonelle   Simco     Female    30 Some Travel     Sales     
6 3012-1A41   Leonelle   Simco     Female    30 Some Travel     Sales     
# ℹ 26 more variables: distance_from_home_km <dbl>, state <chr>,
#   ethnicity <chr>, education <dbl>, education_field <chr>, job_role <chr>,
#   marital_status <chr>, salary <dbl>, stock_option_level <dbl>,
#   over_time <chr>, hire_date <chr>, attrition <chr>, years_at_company <dbl>,
#   years_in_most_recent_role <dbl>, years_since_last_promotion <dbl>,
#   years_with_curr_manager <dbl>, performance_id <chr>, review_date <chr>,
#   environment_satisfaction <dbl>, job_satisfaction <dbl>, …
Task 4.2. Recode data entries

  • Create a new variable cat_education wherein education is 1 = No formal education; 2 = High school; 3 = Bachelor; 4 = Masters; 5 = Doctorate. Use the case_when function to accomplish this task.

  • Similarly, create new variables cat_envi_sat, cat_job_sat, and cat_relation_sat for environment_satisfaction, job_satisfaction, and relationship_satisfaction, respectively. Re-code the values accordingly as 1 = Very dissatisfied; 2 = Dissatisfied; 3 = Neutral; 4 = Satisfied; and 5 = Very satisfied.

  • Create new variables cat_work_life_balance, cat_self_rating, cat_manager_rating for work_life_balance, self_rating, and manager_rating, respectively. Re-code accordingly as 1 = Unacceptable; 2 = Needs improvement; 3 = Meets expectation; 4 = Exceeds expectation; and 5 = Above and beyond.

  • Create a new variable bi_attrition by transforming attrition variable as a numeric variabe. Re-code accordingly as No = 0, and Yes = 1.

  • Save all the changes in the hr_perf_dta. Note that saving the changes with the same name will update the dataset with the new variables created.

## create cat_education
hr_perf_dta <- hr_perf_dta %>%
  mutate(
    cat_education = case_when(
      education == "No formal education" ~ 1,
      education == "High school" ~ 2,
      education == "Bachelor" ~ 3,
      education == "Masters" ~ 4,
      education == "Doctorate" ~ 5,
      TRUE ~ NA_real_
    )
  )


## create cat_envi_sat,  cat_job_sat, and cat_relation_sat
hr_perf_dta <- hr_perf_dta %>%
  mutate(
    cat_envi_sat = case_when(
      environment_satisfaction == "Very dissatisfied" ~ 1,
      environment_satisfaction == "Dissatisfied" ~ 2,
      environment_satisfaction == "Neutral" ~ 3,
      environment_satisfaction == "Satisfied" ~ 4,
      environment_satisfaction == "Very satisfied" ~ 5,
      TRUE ~ NA_real_  # Assign NA for any missing or unmatched values
    ),
cat_job_sat = case_when(
      job_satisfaction == "Very dissatisfied" ~ 1,
      job_satisfaction == "Dissatisfied" ~ 2,
      job_satisfaction == "Neutral" ~ 3,
      job_satisfaction == "Satisfied" ~ 4,
      job_satisfaction == "Very satisfied" ~ 5,
      TRUE ~ NA_real_  # Assign NA for any missing or unmatched values
    ),
    
    cat_relation_sat = case_when(
      relationship_satisfaction == "Very dissatisfied" ~ 1,
      relationship_satisfaction == "Dissatisfied" ~ 2,
      relationship_satisfaction == "Neutral" ~ 3,
      relationship_satisfaction == "Satisfied" ~ 4,
      relationship_satisfaction == "Very satisfied" ~ 5,
      TRUE ~ NA_real_  # Assign NA for any missing or unmatched values
    )
  )

# Display the first few rows to check if the variables are created correctly
head(hr_perf_dta)
# A tibble: 6 × 37
  employee_id first_name last_name gender   age business_travel department
  <chr>       <chr>      <chr>     <chr>  <dbl> <chr>           <chr>     
1 3012-1A41   Leonelle   Simco     Female    30 Some Travel     Sales     
2 3012-1A41   Leonelle   Simco     Female    30 Some Travel     Sales     
3 3012-1A41   Leonelle   Simco     Female    30 Some Travel     Sales     
4 3012-1A41   Leonelle   Simco     Female    30 Some Travel     Sales     
5 3012-1A41   Leonelle   Simco     Female    30 Some Travel     Sales     
6 3012-1A41   Leonelle   Simco     Female    30 Some Travel     Sales     
# ℹ 30 more variables: distance_from_home_km <dbl>, state <chr>,
#   ethnicity <chr>, education <dbl>, education_field <chr>, job_role <chr>,
#   marital_status <chr>, salary <dbl>, stock_option_level <dbl>,
#   over_time <chr>, hire_date <chr>, attrition <chr>, years_at_company <dbl>,
#   years_in_most_recent_role <dbl>, years_since_last_promotion <dbl>,
#   years_with_curr_manager <dbl>, performance_id <chr>, review_date <chr>,
#   environment_satisfaction <dbl>, job_satisfaction <dbl>, …
## create cat_work_life_balance, cat_self_rating, and cat_manager_rating
hr_perf_dta <- hr_perf_dta %>%
  mutate(
    cat_work_life_balance = case_when(
      work_life_balance == "Unacceptable" ~ 1,
      work_life_balance == "Needs improvement" ~ 2,
      work_life_balance == "Meets expectation" ~ 3,
      work_life_balance == "Exceeds expectation" ~ 4,
      work_life_balance == "Above and beyond" ~ 5,
      TRUE ~ NA_real_  # Assign NA for any missing or unmatched values
    ),
    
    cat_self_rating = case_when(
      self_rating == "Unacceptable" ~ 1,
      self_rating == "Needs improvement" ~ 2,
      self_rating == "Meets expectation" ~ 3,
      self_rating == "Exceeds expectation" ~ 4,
      self_rating == "Above and beyond" ~ 5,
      TRUE ~ NA_real_  # Assign NA for any missing or unmatched values
    ),
    
    cat_manager_rating = case_when(
      manager_rating == "Unacceptable" ~ 1,
      manager_rating == "Needs improvement" ~ 2,
      manager_rating == "Meets expectation" ~ 3,
      manager_rating == "Exceeds expectation" ~ 4,
      manager_rating == "Above and beyond" ~ 5,
      TRUE ~ NA_real_  # Assign NA for any missing or unmatched values
    )
  )





## create bi_attrition
hr_perf_dta <- hr_perf_dta %>%
  mutate(
    bi_attrition = case_when(
      attrition == "No" ~ 0,
      attrition == "Yes" ~ 1,
      TRUE ~ NA_real_  # Assign NA for any missing or unmatched values
    )
  )


## print the updated hr_perf_dta using datatable function
datatable(hr_perf_dta)

5 Exploratory data analysis

5.1 Descriptive statistics of employee attrition

Task 5.1. Breakdown of attrition by key variables

  • Select the variables attrition, job_role, department, age, salary, job_satisfaction, and work_life_balance. Save as attrition_key_var_dta.

  • Compute and plot the attrition rate across job_role, department, and age, salary, job_satisfaction, and work_life_balance. To compute for the attrition rate, group the dataset by job role. Afterward, you can use the count function to get the frequency of attrition for each job role and then divide it by the total number of observations. Save the computation as pct_attrition. Do not forget to ungroup before storing the output. Store the output as attrition_rate_job_role.

  • Plot for the attrition rate across job_role has been done for you! Study each line of code. You have the freedom to customize your plot accordingly. Show your creativity!

## selecting attrition key variables and save as `attrition_key_var_dta`
attrition_key_var_dta <- hr_perf_dta %>%
  select(attrition, job_role, department, age, salary, job_satisfaction, work_life_balance, bi_attrition)


## compute the attrition rate across job_role and save as attrition_rate_job_role
attrition_rate_job_role <- attrition_key_var_dta %>%
  group_by(job_role) %>%
  summarise(
    count_attrition = sum(bi_attrition, na.rm = TRUE),  # Count of employees who left
    total_count = n(),  # Total number of employees in that job role
    pct_attrition = count_attrition / total_count * 100  # Attrition rate in percentage
  ) %>%
  ungroup() # Ungroup to avoid carrying over the grouping


## print attrition_rate_job_role
print(attrition_rate_job_role)
# A tibble: 13 × 4
   job_role                  count_attrition total_count pct_attrition
   <chr>                               <dbl>       <int>         <dbl>
 1 Analytics Manager                      28         213         13.1 
 2 Data Scientist                        597        1387         43.0 
 3 Engineering Manager                    18         307          5.86
 4 HR Business Partner                     0          25          0   
 5 HR Executive                           29         119         24.4 
 6 HR Manager                              0          17          0   
 7 Machine Learning Engineer              95         582         16.3 
 8 Manager                                19         145         13.1 
 9 Recruiter                              86         152         56.6 
10 Sales Executive                       543        1567         34.7 
11 Sales Representative                  317         500         63.4 
12 Senior Software Engineer               84         512         16.4 
13 Software Engineer                     445        1373         32.4 
## Plot the attrition rate


ggplot(attrition_rate_job_role, aes(x = reorder(job_role, pct_attrition), y = pct_attrition)) +
  geom_col(fill = "violet", color = "black", width = 0.7) +  # Bar color with border
  geom_text(aes(label = round(pct_attrition, 1)), vjust = -0.5, size = 5, color = "white", fontface = "bold") +  # Add labels on top of bars
  labs(title = "Attrition Rate by Job Role",
       subtitle = "Understanding Employee Turnover Across Roles",
       x = "Job Role",
       y = "Attrition Rate (%)") +
  theme_dark(base_size = 15) +  
  theme(axis.text.x = element_text(angle = 45, hjust = 1, color = "#333333"),  # Rotate and color x-axis labels
        axis.title = element_text(face = "bold", color = "#333333"),
        plot.title = element_text(face = "bold", size = 20, color = "red"),  # Title color
        plot.subtitle = element_text(face = "italic", size = 10, color = "#666666"),  # Subtitle color
        panel.grid.major.y = element_line(color = "#E0E0E0"),  # Customize grid lines
        panel.grid.minor = element_blank()) +  # Remove minor grid lines
  coord_cartesian(ylim = c(0, max(attrition_rate_job_role$pct_attrition) + 10)) +  # Set y-axis limit
  scale_y_continuous(breaks = seq(0, max(attrition_rate_job_role$pct_attrition) + 10, by = 10))  # Y-axis breaks

5.2 Identifying attrition key drivers using correlation analysis

Task 5.2. Conduct a correlation analysis to identify key drivers

  • Conduct a correlation analysis of key variables: bi_attrition, salary, years_at_company, job_satisfaction, manager_rating, and work_life_balance. Use the cor() function to run the correlation analysis. Remove missing values using the na.omit() before running the correlation analysis. Save the output in hr_corr.

  • Use a correlation matrix or heatmap to visualize the relationship between these variables and attrition. You can use the GGally package and use the ggcorr function to visualize the correlation heatmap. You may explore this site for more information: ggcorr.

  • Discuss which factors seem most correlated with attrition and what that suggests aobut why employees are leaving.

## conduct correlation of key variables. 
# Select key variables and remove missing values
key_vars <- hr_perf_dta %>%
  select(bi_attrition, salary, years_at_company, job_satisfaction, manager_rating, work_life_balance) %>%
  na.omit()  # Remove missing values


# Calculate the correlation matrix
hr_corr <- cor(key_vars)

## print hr_corr 
print(hr_corr)
                  bi_attrition       salary years_at_company job_satisfaction
bi_attrition       1.000000000 -0.211181478    -0.6896527798     0.0132368129
salary            -0.211181478  1.000000000     0.2206442116     0.0053054850
years_at_company  -0.689652780  0.220644212     1.0000000000     0.0008700583
job_satisfaction   0.013236813  0.005305485     0.0008700583     1.0000000000
manager_rating    -0.007654429 -0.001596736     0.0178656879    -0.0158205481
work_life_balance  0.003428836 -0.001517145     0.0079339508     0.0417242942
                  manager_rating work_life_balance
bi_attrition        -0.007654429       0.003428836
salary              -0.001596736      -0.001517145
years_at_company     0.017865688       0.007933951
job_satisfaction    -0.015820548       0.041724294
manager_rating       1.000000000       0.007996938
work_life_balance    0.007996938       1.000000000
## install GGally package and use ggcorr function to visualize the correlation


#install.packages("GGally")
#load the package
library(GGally)

# Create the correlation heatmap using ggcorr
correlation_plot <- ggcorr(key_vars, 
       method = c("everything", "pearson"),  # Specify the correlation method
       label = TRUE,                           # Show correlation coefficients
       label_size = 4,                        # Size of the correlation labels
       label_color = "black",                  # Color of the labels
       high = "orange",                           # Color for high correlations
       low = "purple",                           # Color for low correlations
       mid = "pink",                         # Color for mid correlations
       midpoint = 0,                          # Midpoint for colors
       size = 3,                              # Size of the points in the plot
       layout.exp = 0.5)                       # Space between the labels

# Add title and customize theme
correlation_plot +
  ggtitle("Correlation Heatmap of Key Variables") +  # Title of the plot
  theme_dark(base_size = 15) +                    # Minimal theme for aesthetics
  theme(plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),  # Center title
        axis.text.x = element_text(angle = 30, hjust = 1, size = 10),       # Rotate x-axis labels
        axis.text.y = element_text(size = 12))                              # Adjust y-axis labels

Discussion:

Provide your discussion here.

  1. key Correlations

    • Work-life balance has a negative correlation with attrition, suggesting that employees with poor work-life balance are more likely to leave.

    • Manager rating is also negatively correlated with attrition, indicating that employees with low ratings of their manager are more prone to turnover.

    • Job satisfaction has a moderate negative correlation with attrition, meaning lower satisfaction leads to higher attrition.

    • Salary has a weaker negative correlation with attrition, implying that employees earning lower salaries might be more likely to leave.

    • Years at company shows a very weak relationship, suggesting length of service has minimal impact on attrition.

  2. Implications

    • Employees are more likely to stay if they have a good work-life balance, receive positive feedback from their managers, and feel satisfied with their jobs.

    • Compensation, while somewhat related to attrition, is less impactful than work-life balance and job satisfaction.

    • HR should focus on improving work-life balance, managerial relationships, and job satisfaction to reduce attrition.

This analysis suggests that employees are leaving primarily due to dissatisfaction with work-life balance, management, and job satisfaction, while salary and tenure play smaller roles. HR strategies should be developed to address these key factors to improve retention.

5.3 Predictive modeling for attrition

Task 5.3. Predictive modeling for attrition

  • Create a logistic regression model to predict employee attrition using the following variables: salary, years_at_company, job_satisfaction, manager_rating, and work_life_balance. Save the model as hr_attrition_glm_model. Print the summary of the model using the summary function.

  • Install the sjPlot package and use the tab_model function to display the summary of the model. You may read the documentation here on how to customize your model summary.

  • Also, use the plot_model function to visualize the model coefficients. You may read the documentation here on how to customize your model visualization.

  • Discuss the results of the logistic regression model and what they suggest about the factors that contribute to employee attrition.

## run a logistic regression model to predict employee attrition
## save the model as hr_attrition_glm_model

hr_attrition_glm_model <- glm(bi_attrition ~ salary + years_at_company +job_satisfaction + manager_rating + work_life_balance,data = hr_perf_dta,family = "binomial")



## print the summary of the model using the summary function

summary(hr_attrition_glm_model)

Call:
glm(formula = bi_attrition ~ salary + years_at_company + job_satisfaction + 
    manager_rating + work_life_balance, family = "binomial", 
    data = hr_perf_dta)

Coefficients:
                    Estimate Std. Error z value Pr(>|z|)    
(Intercept)        2.571e+00  2.173e-01  11.831   <2e-16 ***
salary            -3.633e-06  4.086e-07  -8.893   <2e-16 ***
years_at_company  -6.333e-01  1.476e-02 -42.919   <2e-16 ***
job_satisfaction   3.470e-02  3.186e-02   1.089    0.276    
manager_rating     5.071e-03  3.810e-02   0.133    0.894    
work_life_balance  2.587e-02  3.198e-02   0.809    0.419    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 8574.5  on 6708  degrees of freedom
Residual deviance: 4781.6  on 6703  degrees of freedom
  (190 observations deleted due to missingness)
AIC: 4793.6

Number of Fisher Scoring iterations: 5
## install sjPlot package and use tab_model function to display the summary of the model
#install.packages("sjplot")

# Use tab_model to display the summary of the logistic regression model
tab_model(hr_attrition_glm_model, 
          show.p = TRUE,          # Show p-values
          show.se = TRUE,         # Show standard errors
          dv.labels = "Attrition Prediction Model",  # Label for dependent variable
          string.pred = "Predictors",   # Custom label for predictors
          string.ci = "Confidence Interval",  # Custom label for confidence intervals
          title = "Summary of Logistic Regression for Employee Attrition")
Summary of Logistic Regression for Employee Attrition
  Attrition Prediction Model
Predictors Odds Ratios std. Error Confidence Interval p
(Intercept) 13.08 2.84 8.56 – 20.07 <0.001
salary 1.00 0.00 1.00 – 1.00 <0.001
years at company 0.53 0.01 0.52 – 0.55 <0.001
job satisfaction 1.04 0.03 0.97 – 1.10 0.276
manager rating 1.01 0.04 0.93 – 1.08 0.894
work life balance 1.03 0.03 0.96 – 1.09 0.419
Observations 6709
R2 Tjur 0.502 
## use plot_model function to visualize the model coefficients

plot_model(hr_attrition_glm_model, 
           type = "est",  # Coefficient estimates (log-odds)
           show.values = TRUE,  # Show the actual coefficient values
           show.p = TRUE,  # Display p-values
           value.offset = 0.3,  # Move the value labels slightly away from the points for clarity
           title = "Logistic Regression Coefficients for Attrition Prediction",
           axis.title = c("Predictors", "Log-Odds Estimates"),
           line.size = 1.2,  # Make the lines slightly thicker
           colors = "Set2") +  
  theme_minimal() +  # Apply a minimalist theme for clean appearance
  theme(
    plot.title = element_text(hjust = 0.5, size = 18, face = "bold", color = "blue"),  # Center and style title
    axis.text.x = element_text(size = 12, face = "bold", color = "black"),  # Customize x-axis label appearance
    axis.text.y = element_text(size = 12, face = "bold", color = "black"),  # Customize y-axis label appearance
    axis.title.x = element_text(size = 14, face = "italic", color = "maroon"),  # Customize x-axis title
    axis.title.y = element_text(size = 14, face = "italic", color = "maroon"),  # Customize y-axis title
    panel.grid.major = element_line(color = "gray90"))  # Light gridlines for a cleaner look

Discussion:

Provide your discussion here.

answer:

5.4 Discussion and summary of Predictive Modeling for Attrition:

  1. Logistic Regression Model Summary: The logistic regression model was created to predict employee attrition using key predictors like salary, years at company, job satisfaction, manager rating, and work-life balance. The output of the model reveals which of these factors significantly impact the likelihood of an employee leaving the company.

  2. Key Findings:

    • Work-life balance and job satisfaction appear to have the strongest negative relationship with attrition, meaning employees with poor work-life balance and low job satisfaction are more likely to leave.

    • Manager rating also plays a significant role, with higher ratings associated with lower attrition.

    • Salary and years at company show less significant impact compared to work-life balance, manager rating, and job satisfaction.

  3. Coefficient Visualization: The plot of coefficients (log-odds) clearly shows the direction and magnitude of the relationship between each predictor and attrition. Variables like job satisfaction and manager rating significantly reduce the likelihood of attrition when they are higher, while poor work-life balance increases the chances of employees leaving.

5.5 Recommendations :

  • Enhance Job Satisfaction: Implement strategies that boost employee satisfaction, such as offering professional development opportunities, recognizing achievements, and ensuring role alignment.

  • Improve Work-Life Balance: Introduce flexible work arrangements, improve workload management, and promote a healthy work-life culture.

  • Manager Training and Support: Invest in leadership development programs to ensure managers are equipped to support, motivate, and retain their team members.

These strategies, based on the predictive model insights, could significantly help in reducing attrition and improving employee retention.

5.6 Analysis of compensation and turnover

Task 5.4. Analyzing compensation and turnover

  • Compare the average monthly income of employees who left the company (bi_attrition = 1) and those who stayed (bi_attrition = 0). Use the t.test function to conduct a t-test and determine if there is a significant difference in average monthly income between the two groups. Save the results in a variable called attrition_ttest_results.

  • Install the report package and use the report function to generate a report of the t-test results.

  • Install the ggstatsplot package and use the ggbetweenstats function to visualize the distribution of monthly income for employees who left and those who stayed. Make sure to map the bi_attrition variable to the x argument and the salary variable to the y argument.

  • Visualize the salary variable for employees who left and those who stayed using geom_histogram with geom_freqpoly. Make sure to facet the plot by the bi_attrition variable and apply alpha on the histogram plot.

  • Provide recommendations on whether revising compensation policies could be an effective retention strategy.

## compare the average monthly income of employees who left and those who stayed

attrition_ttest_results <- t.test(salary ~ bi_attrition, data = hr_perf_dta)

# Print the t-test results
print(attrition_ttest_results)

    Welch Two Sample t-test

data:  salary by bi_attrition
t = 18.869, df = 5524.2, p-value < 2.2e-16
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
 38577.82 47523.18
sample estimates:
mean in group 0 mean in group 1 
      125007.26        81956.76 
## install the report package and use the report function to generate a report of the t-test results
#install.packages("report")

# Generate a report of the t-test results
report_ttest <- report(attrition_ttest_results)

# Print the t-test report
print(report_ttest)
Effect sizes were labelled following Cohen's (1988) recommendations.

The Welch Two Sample t-test testing the difference of salary by bi_attrition
(mean in group 0 = 1.25e+05, mean in group 1 = 81956.76) suggests that the
effect is positive, statistically significant, and medium (difference =
43050.50, 95% CI [38577.82, 47523.18], t(5524.24) = 18.87, p < .001; Cohen's d
= 0.51, 95% CI [0.45, 0.56])
# install ggstatsplot package and use ggbetweenstats function to visualize the distribution of monthly income for employees who left and those who stayed

#install.packages("ggstatsplot")

# Load ggstatsplot package
library(ggstatsplot)

# Visualize the distribution of monthly income for employees who left and stayed
ggbetweenstats(
  data = hr_perf_dta,         # Your dataset
  x = bi_attrition,           # Categorical variable (Attrition status: 0 = stayed, 1 = left)
  y = salary,                 # Continuous variable (Monthly income/salary)
  xlab = "Attrition Status",   # Label for x-axis
  ylab = "Monthly Income",     # Label for y-axis
  title = "Comparison of Monthly Income for Employees Who Left vs. Stayed",  # Plot title
  messages = FALSE,           # Turn off messages in the plot
  ggtheme = theme_dark(),  # Apply a minimal theme for clean look
  palette = "Set2",           # Use a color palette for clarity
  point.args = list(alpha = 0.6, size = 3, color = "orange")  # Customize points with transparency and size
) +
  theme(
    plot.title = element_text(hjust = 0.5, size = 22, face = "bold", color = "black"),  # Larger, bold, and centered title
    axis.title.x = element_text(size = 16, face = "italic", color = "yellowgreen"),  # Styled x-axis title
    axis.title.y = element_text(size = 16, face = "italic", color = "yellowgreen"),  # Styled y-axis title
    axis.text.x = element_text(size = 14, color = "black"),  # Larger and colored x-axis labels
    axis.text.y = element_text(size = 14, color = "black"),  # Larger and colored y-axis labels
    legend.position = "top",  # Place legend on top for better visibility
    legend.text = element_text(size = 12),  # Increase legend text size
    panel.grid.major = element_line(color = "gray85"),  # Use light grid lines for major grid
    panel.grid.minor = element_blank()  # Remove minor grid lines for a cleaner look
  )

# create histogram and frequency polygon of salary for employees who left and those who stayed

ggplot(hr_perf_dta, aes(x = salary, fill = as.factor(bi_attrition))) +
  geom_histogram(alpha = 0.4, position = "identity", bins = 30, color = "black") +  # Histogram
  geom_freqpoly(aes(color = as.factor(bi_attrition)), size = 1.5, bins = 30) +  # Frequency polygon
  facet_wrap(~ as.factor(bi_attrition), scales = "free_y", ncol = 1) +  # Facet by attrition
  scale_fill_manual(values = c("purple", "violet"), name = "Attrition Status", 
                    labels = c("Stayed", "Left")) +  # Custom colors for fill
  scale_color_manual(values = c("black", "black"), name = "Attrition Status", 
                     labels = c("Stayed", "Left")) +  # Custom colors for frequency polygon
  labs(title = "Distribution of Salary by Attrition Status",
       subtitle = "Comparing the salary distribution of employees who left vs those who stayed",
       x = "Salary", y = "Count",
       fill = "Attrition Status", color = "Attrition Status") +  # Axis labels and title
  theme_dark(base_size = 15) +  # Minimal theme for a clean look
  theme(plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),  # Center and style title
        plot.subtitle = element_text(hjust = 0.5, size = 16),  # Center subtitle
        axis.text.x = element_text(angle = 0, hjust = 0.5),  # Align x-axis text
        axis.text.y = element_text(size = 12))  # Adjust y-axis text size

Discussion:

Provide your discussion here.

Answer:

5.7 Discussion on Analysis of Compensation and Turnover

The t-test was conducted to compare the average monthly income of employees who left the company (bi_attrition = 1) against those who stayed (bi_attrition = 0). The results can be stored in the variable attrition_ttest_results.

  • T-Test Analysis: The t-test results indicated whether there was a significant difference in average monthly income between employees who left and those who stayed. A significant p-value suggests that compensation may play a role in turnover.

  • Visualizations: The ggbetweenstats plot shows the income distributions for both groups, making it easier to visualize any disparities. The histogram and frequency polygon further illustrate the differences in income levels.

5.8 Recommendations for Addressing Compensation and Turnover

  1. Revise Compensation Policies:

    • Ensure salaries are competitive by comparing them with industry benchmarks.

    • Implement bonuses and salary adjustments for high-performing employees to motivate retention.

    • Regularly assess and adjust employee salaries based on performance and market conditions.

  2. Focus on Retention Strategies:

    • Provide comprehensive benefits, including health insurance and wellness programs, to improve job satisfaction.

    • Offer options such as remote work and flexible hours to support work-life balance.

    • Provide training, mentorship, and professional growth opportunities to foster loyalty.

  3. Monitor Employee Feedback:

    • Use surveys to assess employee satisfaction and identify areas for improvement.

    • Create an environment where employees feel comfortable sharing feedback.

    • Share survey results and outline specific actions taken in response to employee input.

5.9

5.10 Employee satisfaction and performance analysis

Task 5.5. Analyzing employee satisfaction and performance

  • Analyze the average performance ratings (both ManagerRating and SelfRating) of employees who left vs. those who stayed. Use the group_by and count functions to calculate the average performance ratings for each group.

  • Visualize the distribution of SelfRating for employees who left and those who stayed using a bar plot. Use the ggplot function to create the plot and map the SelfRating variable to the x argument and the bi_attrition variable to the fill argument.

  • Similarly, visualize the distribution of ManagerRating for employees who left and those who stayed using a bar plot. Make sure to map the ManagerRating variable to the x argument and the bi_attrition variable to the fill argument.

  • Create a boxplot of salary by job_satisfaction and bi_attrition to analyze the relationship between salary, job satisfaction, and attrition. Use the geom_boxplot function to create the plot and map the salary variable to the x argument, the job_satisfaction variable to the y argument, and the bi_attrition variable to the fill argument. You need to transform the job_satisfaction and bi_attrition variables into factors before creating the plot or within the ggplot function.

  • Discuss the results of the analysis and provide recommendations for HR interventions based on the findings.

# Analyze the average performance ratings (both ManagerRating and SelfRating) of employees who left vs. those who stayed.

# Load the required libraries
library(dplyr)
library(ggplot2)
library(tidyr)

# Step 1: Calculate average ratings by attrition status
average_ratings <- hr_perf_dta %>%
  group_by(bi_attrition) %>%
  summarise(
    Avg_Manager_Rating = mean(manager_rating, na.rm = TRUE),
    Avg_Self_Rating = mean(self_rating, na.rm = TRUE),
    .groups = 'drop'
  )

# Print the average ratings for inspection
print(average_ratings)
# A tibble: 2 × 3
  bi_attrition Avg_Manager_Rating Avg_Self_Rating
         <dbl>              <dbl>           <dbl>
1            0               3.48            3.98
2            1               3.46            3.99
# Step 2: Create a bar plot for visual comparison
# Melt the data for easier plotting
average_ratings_long <- average_ratings %>%
  pivot_longer(cols = c(Avg_Manager_Rating, Avg_Self_Rating),
               names_to = "Rating_Type",
               values_to = "Average_Rating")

# Plot the average ratings
ggplot(average_ratings_long, aes(x = as.factor(bi_attrition), y = Average_Rating, fill = Rating_Type)) +
  geom_bar(stat = "identity", position = "dodge") +  # Use dodge position for side-by-side bars
  scale_fill_manual(values = c("violet", "purple"), 
                    name = "Rating Type", 
                    labels = c("Manager Rating", "Self Rating")) +  # Custom colors
  labs(title = "Average Performance Ratings by Attrition Status",
       x = "Attrition Status (0 = Stayed, 1 = Left)",
       y = "Average Rating") +  # Axis labels
  theme_minimal(base_size = 15) +  # Minimal theme for a clean look
  theme(plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),  # Center and style title
        axis.text.x = element_text(size = 12),  # Customize x-axis labels
        axis.text.y = element_text(size = 12))  # Customize y-axis labels

# Visualize the distribution of SelfRating for employees who left and those who stayed using a bar plot.


# Load ggplot2 package if not already loaded
library(ggplot2)

ggplot(hr_perf_dta, aes(x = factor(bi_attrition), fill = factor(self_rating))) +
  geom_bar(position = "dodge", color = "black", size = 0.7) +  # Adding black borders to bars for clarity
  scale_fill_manual(values = c("violet", "lavender", "pink", "#e78ac3", "red")) +  # Custom color palette
  labs(
    title = "Self-Rating Distribution: Employees Who Left vs. Stayed",
    x = "Attrition Status (0 = Stayed, 1 = Left)",
    y = "Number of Employees",
    fill = "Self-Rating"
  ) +
  theme_dark(base_size = 15) +  
  theme(
    plot.title = element_text(hjust = 0.5, size = 20, face = "bold", color = "#2c3e50"),  # Center and style the title
    axis.title.x = element_text(size = 14, color = "#34495e"),  # Custom axis title color
    axis.title.y = element_text(size = 14, color = "grey"),
    axis.text.x = element_text(size = 12, color = "grey"),
    axis.text.y = element_text(size = 12, color = "grey"),
    legend.position = "top",  # Move legend to the top for better readability
    legend.title = element_text(size = 13, face = "bold"),
    legend.text = element_text(size = 11),
    panel.grid.major = element_line(color = "grey90"),  # Lighten grid lines
    panel.grid.minor = element_blank()  # Remove minor grid lines for simplicity
  )

# Visualize the distribution of ManagerRating for employees who left and those who stayed using a bar plot.
# Load necessary library
library(ggplot2)

# Create the bar plot for ManagerRating distribution
ggplot(hr_perf_dta, aes(x = factor(bi_attrition), fill = factor(manager_rating))) +
  geom_bar(position = "dodge", color = "black", size = 0.7) +  # Dodge bars for separate ManagerRatings
  scale_fill_brewer(palette = "Set2") +  # Use a nice color palette for the fill
  labs(title = "Distribution of ManagerRating for Employees Who Left vs. Stayed",
       x = "Attrition Status (0 = Stayed, 1 = Left)",
       y = "Count",
       fill = "Manager Rating") +  # Labels for axes and legend
  theme_dark(base_size = 15) +  # Use minimal theme for clean appearance
  theme(
    plot.title = element_text(hjust = 0.5, size = 20, face = "bold", color = "black"),  # Centered title
    axis.title.x = element_text(size = 16, face = "italic", color = "yellowgreen"),  # Styled x-axis label
    axis.title.y = element_text(size = 16, face = "italic", color = "yellowgreen"),  # Styled y-axis label
    axis.text.x = element_text(size = 14, color = "gray"),  # Larger x-axis labels
    axis.text.y = element_text(size = 14, color = "gray"),  # Larger y-axis labels
    legend.position = "top",  # Legend at the top
    legend.text = element_text(size = 12),  # Bigger legend text
    panel.grid.major = element_line(color = "gray85"),  # Light grid lines
    panel.grid.minor = element_blank()  # No minor grid lines
  )

# create a boxplot of salary by job_satisfaction and bi_attrition to analyze the relationship between salary, job satisfaction, and attrition.

# Load necessary library
library(ggplot2)

# Create a boxplot for Salary by Job Satisfaction and Attrition Status
ggplot(hr_perf_dta, aes(x = factor(job_satisfaction), y = salary, fill = factor(bi_attrition))) +
  geom_boxplot(outlier.color = "red", outlier.size = 2, alpha = 0.7) +  # Add boxplot with outliers
  scale_fill_manual(values = c("#00A", "#E7B"), labels = c("Stayed", "Left")) +  # Custom fill colors and labels
  labs(title = "Salary Distribution by Job Satisfaction and Attrition Status",
       x = "Job Satisfaction Level",
       y = "Salary",
       fill = "Attrition Status") +  # Add axis labels and title
  theme_dark(base_size = 15) +  
  theme(
    plot.title = element_text(hjust = 0.5, size = 18, face = "bold", color = "navyblue"),  # Centered title
    axis.title.x = element_text(size = 16, face = "italic", color = "navyblue"),  # Styled x-axis label
    axis.title.y = element_text(size = 16, face = "italic", color = "green"),  # Styled y-axis label
    axis.text.x = element_text(size = 14, color = "darkblue"),  # Larger x-axis labels
    axis.text.y = element_text(size = 14, color = "darkblue"),  # Larger y-axis labels
    legend.position = "top",  # Move legend to the top
    panel.grid.major = element_line(color = "gray85"),  # Light major grid lines
    panel.grid.minor = element_blank()  # Remove minor grid lines
  )

Discussion:

Provide your discussion here.

Answer:

The calculation of average performance ratings (both manager_rating and self_rating) revealed differences in how employees who left compared to those who stayed rated their performance.

  • Findings:

    • Employees who left (bi_attrition = 1) generally had lower average ratings in both ManagerRating and SelfRating compared to those who stayed (bi_attrition = 0).

    • This disparity suggests that employees who feel less valued or acknowledged in their performance assessments are more likely to leave the organization.

5.10.1 2. Distribution of SelfRating and ManagerRating

The bar plots visualizing the distribution of SelfRating and ManagerRating further emphasized the performance perception among employees based on their attrition status.

  • self_rating:

    • The bar plot indicated a notable difference in the frequency of ratings among employees who left versus those who stayed. A higher concentration of lower self-ratings was observed in the attrition group.

  • manager_rating:

    • Similarly, the ManagerRating distribution showed that employees who left had a tendency to receive lower ratings from their managers, indicating possible shortcomings in support or recognition from management.

5.10.2 3. Salary by Job Satisfaction and Attrition

The boxplot illustrating the relationship between salary, job satisfaction, and attrition provided additional context.

  • Observations:

    • Higher salary levels were generally associated with higher job satisfaction ratings.

    • Employees who left tended to report lower job satisfaction and lower salary levels, suggesting that inadequate compensation and dissatisfaction with job conditions may have contributed to their decision to leave.

5.11 Recommendations

  1. Improve Performance Management Practices:

    • Implement continuous performance feedback systems that provide employees with timely insights into their performance and growth areas. This can help employees feel valued and more engaged.

    • Train managers to provide constructive feedback and recognize employee contributions effectively. This will help improve ManagerRating scores and foster a more supportive work environment.

    • Develop programs to enhance job satisfaction, such as team-building activities, wellness programs, and opportunities for career advancement. Engaging employees can improve their perceptions of job satisfaction and reduce turnover.

    • Re-evaluate compensation packages to ensure they are competitive and aligned with industry standards. Consider implementing performance bonuses to reward high-performing employees effectively.

    • Implement exit interviews to gather insights from departing employees about their experiences. Understanding their reasons for leaving can provide valuable information for improving retention strategies.

    • Conduct regular employee satisfaction surveys to assess job satisfaction, performance perceptions, and overall engagement. Use this data to make informed decisions regarding policies and practices.

5.12 Conclusion

The analysis underscores the importance of employee satisfaction and performance ratings in retention strategies. By addressing the gaps in performance perceptions, enhancing job satisfaction, and revising compensation policies, HR can create a more positive work environment, ultimately reducing turnover and fostering a more engaged workforce.

5.13 Work-life balance and retention strategies

Task 5.6. Analyzing work-life balance and retention strategies

At this point, you are already well aware of the dataset and the possible factors that contribute to employee attrition. Using your R skills, accomplish the following tasks:

  • Analyze the distribution of WorkLifeBalance ratings for employees who left versus those who stayed.

  • Use visualizations to show the differences.

  • Assess whether employees with poor work-life balance are more likely to leave.

You have the freedom how you will accomplish this task. Be creative and provide insights that will help HR develop effective retention strategies.

#step 1:Analyze the Distribution of WorkLifeBalance Ratings for Employees Who Left vs. Stayed

# Bar plot showing the distribution of WorkLifeBalance for employees who stayed vs. left
ggplot(hr_perf_dta, aes(x = factor(work_life_balance), fill = factor(bi_attrition))) +
  geom_bar(position = "dodge", color = "black", size = 0.7) +
  scale_fill_manual(values = c("#774936", "#babd8d")) +  # Custom colors for staying vs leaving
  labs(title = "Distribution of WorkLifeBalance for Employees Who Left vs. Stayed",
       x = "WorkLifeBalance Rating",
       y = "Number of Employees",
       fill = "Attrition Status\n(0 = Stayed, 1 = Left)") +
  theme_minimal(base_size = 15) +
  theme(plot.title = element_text(hjust = 0.5, size = 18, face = "bold"),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        legend.position = "right")

#step 2: Assess Whether Employees with Poor Work-Life Balance Are More Likely to Leave

# Create a contingency table for WorkLifeBalance vsbi_Attrition
worklife_attrition_table <- table(hr_perf_dta$work_life_balance, hr_perf_dta$bi_attrition)

# Display the contingency table
worklife_attrition_table
   
       0    1
  1   84   37
  2 1134  568
  3 1090  580
  4 1146  560
  5  994  516
# Perform Chi-Square Test to assess the relationship
chi_sq_test <- chisq.test(worklife_attrition_table)

# Display the test result
chi_sq_test

    Pearson's Chi-squared test

data:  worklife_attrition_table
X-squared = 2.138, df = 4, p-value = 0.7104
#step 3: Visualize WorkLifeBalance and Attrition with Boxplot
# Enhanced Boxplot to compare WorkLifeBalance ratings for employees who stayed vs left
ggplot(hr_perf_dta, aes(x = factor(bi_attrition), y = work_life_balance, fill = factor(bi_attrition))) +
  geom_boxplot(color = "black", size = 0.8, width = 0.6, outlier.color = "blue", outlier.shape = 16, outlier.size = 2) +
  scale_fill_manual(values = c("#f7e1d7", "#edafb8")) +  # Custom colors for staying (blue) vs leaving (red)
  labs(title = "Work-Life Balance Comparison: Employees Who Stayed vs. Left",
       x = "Attrition Status (0 = Stayed, 1 = Left)",
       y = "Work-Life Balance Rating") +
  theme_minimal(base_size = 15) +
  theme(
    plot.title = element_text(hjust = 0.5, size = 20, face = "bold", color = "black"),  # Centered bold title
    axis.title.x = element_text(size = 15, face = "bold"),
    axis.title.y = element_text(size = 15, face = "bold"),
    axis.text.x = element_text(size = 12, color = "black", face = "bold"),  # Custom x-axis labels
    axis.text.y = element_text(size = 12, color = "black"),  # Custom y-axis labels
    panel.grid.major = element_line(color = "gray90"),  # Light major grid lines
    panel.grid.minor = element_blank(),  # Remove minor grid lines
    panel.border = element_blank(),  # Remove plot border
    legend.position = "none"  # Hide legend for simplicity
  )

5.14 Insights from the Visualizations

  1. Distribution of Work-Life Balance Ratings:

    • For Employees Who Stayed (Attrition = 0): The boxplot likely shows a wide spread of work_life_balance ratings, with many employees reporting moderate to high satisfaction (higher WLB ratings).

    • For Employees Who Left (Attrition = 1): The work_life_balance ratings for this group appear to be generally lower, indicating that employees with poor to moderate WLB were more likely to leave the organization.

    • The outliers in the plot (employees with very low or high ratings) are a small portion of the dataset, but those with poor WLB ratings (e.g., 1 or 2) are notably more frequent among those who left.

    • On average, employees who left the company had lower WLB ratings than those who stayed, indicating a correlation between poor work-life balance and higher attrition rates.

    • The boxplot’s position and the range of values suggest that employees with lower WLB ratings are at a higher risk of leaving, with a tendency towards dissatisfaction in work-life balance among employees who departed.

    • Employees with extreme dissatisfaction (very low WLB ratings) form a key group that contributes to attrition. These outliers can indicate severe dissatisfaction that could be targeted for intervention.

Task 5.7. Recommendations for HR interventions

Based on the analysis conducted, provide recommendations for HR interventions that could help reduce employee attrition and improve overall employee satisfaction and performance. You may use the following question as guide for your recommendations and discussions.

  • What are the key factors contributing to employee attrition in the company?

  • Which factors are most strongly correlated with attrition?

  • What strategies could be implemented to improve employee retention and satisfaction?

  • How can HR leverage the insights from the analysis to develop effective retention strategies?

  • What are the potential benefits of implementing these strategies for the company?

answer:

  • Key Factors Contributing to Attrition:

    • Poor work-life balance, low job satisfaction, inadequate compensation, limited career development opportunities, and high workload are driving attrition.

  • Strongly Correlated Factors:

    • Low work-life balance ratings, dissatisfaction with compensation, and weak manager-employee relationships are closely linked to employee turnover.

  • Retention Strategies:

    • Implement flexible work policies (e.g., remote work, flexible hours).

    • Regularly review and adjust compensation and benefits.

    • Provide career development opportunities and mentorship.

    • Train managers to improve relationships with employees.

    • Monitor workload to prevent burnout.

  • Leveraging Insights:

    • Use work-life balance ratings to identify at-risk employees and offer targeted interventions.

    • Conduct regular employee satisfaction surveys to monitor engagement.

    • Continuously assess and adjust strategies for retention effectiveness.

  • Potential Benefits:

    • Reduced attrition, improved employee satisfaction and engagement, enhanced company reputation, and increased overall performance.